2Learning ability can be substantially improved by artificial selection in animalsranging from Drosophila to rats. Thus these species have not used theirevolutionary potential with respect to learning ability, despite intuitively expectedand experimentally demonstrated adaptive advantages of learning. This suggeststhat learning is costly, but this notion has rarely been tested. Here we reportcorrelated responses of life-history traits to selection for improved learning inDrosophila melanogaster. Replicate populations selected for improved learninglived on average 15% shorter than the corresponding unselected controlpopulations. They also showed a minor reduction in fecundity late in life andpossibly a minor increase in dry adult mass. Selection for improved learning hadno effect on egg-to-adult viability, development rate or desiccation resistance.Because shortened longevity was the strongest correlated response to selectionfor improved learning, we also measured learning ability in another set ofreplicate populations that had been selected for extended longevity. In a classicalolfactory conditioning assay, these long-lived flies showed an almost 40%reduction in learning ability early in life. This effect disappeared with age. Ourresults suggest a symmetrical evolutionary trade-off between learning ability andlongevity in Drosophila.

3RunBot is a biped robot that can adapt its walking pattern to terrain changes(Manoonpong et al. 2007). This ability is not encoded in preexisting algorithms. Instead,an experience such as falling will strengthen synapses between artificial sensor neuronsand motor neurons, resulting in an improved response to sensory input. Althoughdesigned as a model to understand locomotion and coordination, RunBot illustrates theadaptive power of learning. In the natural world, a classical example of the benefits oflearning is the ability of predators to learn to associate aposematic signals with danger(Marples et al. 2005). This benefits the predator, although the predator’s learning abilitycan also be exploited by Batesian mimics. The ability to learn also increases a predator’sforaging efficiency, e.g. through search image formation (Pietrewicz and Kamil 1979).Other examples of positive effects of learning on fitness have been found in blue tits(Grieco et al. 2002), grasshoppers (Dukas and Bernays 2000), parasitoid wasps (Dukasand Duan 2000; Steidle 1998), fruit flies (Dukas 2005), herbivorous mites (Egas andSabelis 2001) and honeybees (Barron et al. 2007; Sherman and Visscher 2002). Learningis likely to be advantageous over genetically determined (innate) responses under certainconditions, such as spatial or temporal heterogeneity (Dukas et al. 2006; Luttbeg andWarner 1999; Papaj and Prokopy 1989; Stephens 1993).Learning ability has been substantially improved by artificial selection in rats (Tryon1940), blowflies (McGuire and Hirsch 1977), honeybees (Brandes 1988) and Drosophila(Lofdahl et al. 1992; Mery and Kawecki 2002). This suggests that at least some specieshave not fully realized the genetic potential for learning ability. One possible explanationis that natural selection favors intermediate levels of learning ability, because furtherimprovements would be too costly. Yet, experimental data addressing the existence andnature of costs of learning are still scarce.It is useful to distinguish between constitutive and operating costs of learning ability.Constitutive (genetic) costs are paid by individuals with a higher learning abilityirrespective of whether or not this ability is actually used. These costs presumably resultfrom developing and maintaining a sensory and nervous system (Dukas 1999). Operatingcosts are paid only when the sensory and nervous systems are actually used to learn.These costs likely reflect the metabolic resources allocated to acquisition, retention andretrieval of information (Laughlin 2001). In addition to trade-offs between learning andother traits, there might be negative correlations between different forms of memory(Isabel et al. 2004; Mery et al. 2007a). A number of experimental studies have foundoperating costs of learning, such as reduced immunity in mice (Barnard et al. 2006),reduced egg-laying rate in Drosophila (Mery and Kawecki 2004) and reduced desiccationresistance in Drosophila (Mery and Kawecki 2005). However, the only experimentalevidence for a constitutive, genetically based cost of learning is reduced larvalcompetitive ability in Drosophila (Mery and Kawecki 2003).In this study we focus on constitutive costs resulting from genetic trade-offs betweenlearning ability and fitness-related life-history traits. We address this issue by studyingcorrelated responses to selection in two sets of experimentally evolved populations ofDrosophila melanogaster. We first set out to test for correlated responses to selection forimproved learning. We used replicate populations that had been subject to selection forthe ability to learn an association between an oviposition substrate and bitter taste, and tocontinue avoiding this substrate even when the bitter taste was no longer present (Meryand Kawecki 2002). These selected populations evolved markedly improved aversive

4learning ability, manifested not only in the original oviposition task used to imposeselection, but also in a classical olfactory conditioning assay (Mery et al. 2007b). Wecompared a number of fitness-related traits (age-specific fecundity and mortality, egg-to-adult survival, development rate, adult body mass and desiccation resistance) betweenthese “high-learning” populations and the corresponding unselected control populations.The most striking correlated response we found was a reduction in longevity. Therefore,to test the robustness of this apparent trade-off, we assayed aversive learning in a set ofpopulations selected for late-age reproduction (Arking 1987; Luckinbill et al. 1984).Compared to the corresponding unselected controls, these long-lived flies showed areduction in learning performance as a correlated response, providing additional evidencefor an evolutionary trade-off between learning ability and longevity.

Methods

SELECTION FOR IMPROVED LEARNING

Fly origin and maintenanceWe used seven “high-learning” selected populations and six unselected controlpopulations. Their history has been described by Mery and Kawecki (2002). In short, abase population was founded from 2000 flies collected in Basel, Switzerland in 1999.Selection on learning ability was imposed each generation by offering flies a choicebetween two oviposition substrates (orange and pineapple jelly). During a 3-h trainingperiod, one of the substrates was supplemented with a bitter flavor (quinine) to provideflies with an opportunity to associate the substrate with an aversive taste. During asubsequent 3-h testing period, flies were offered a choice between the two substrateswithout quinine. Eggs for the next generation were collected from the substrate that hadnot contained quinine during the training period. In that way, flies that rememberedwhich substrate had been associated with bitter taste and continued to prefer the othersubstrate for oviposition contributed more eggs to the next generation. For ourexperiments flies were reared under the same conditions as those used in the course ofselection, i.e. a yeast-sucrose-cornmeal medium with 1% w/w brewer’s yeast (ActilifeFitovit), controlled density of 200 eggs per 30 ml medium, 25°C and 60% relativehumidity.

Direct response: oviposition learning assayWithin 30 generations of selection, the high-leaning populations evolved substantiallybetter performance in the oviposition learning test (Mery and Kawecki 2002). Subsequenttests showed that they also performed better in a Pavlovian shock-odor learning assay(Mery et al. 2007b); we use this assay below to study learning in populations selected forincreased longevity. However, because another fifty generations have passed, we wantedto confirm that flies selected for improved learning still learned better than the controls.We therefore measured learning ability using an oviposition learning assay modified fromMery and Kawecki (2002). As in the course of selection, we used orange and pineapplesubstrates (8 g agar per liter juice with a drop of fresh baker’s yeast) as conditionedstimuli, and quinine hydrochloride (7 g/l) as an aversive unconditioned stimulus. Twodays before the assay, flies were offered fresh baker’s yeast to stimulate egg production.During the assay, flies were first trained by keeping them for 45 min in a 175-ml vialwith substrate A (orange or pineapple) without quinine, and then for another 45 min in a

5vial with substrate B (pineapple or orange, respectively) supplemented with quinine.Subsequently, flies were tested by allowing them to oviposit for 2 hours in a cage (l×w×h= 19×12×12 cm) containing both substrates without quinine. As is standard in flyolfactory learning assays, both training and testing took place in the dark to preventconfounding effects of phototaxis. We measured the conditioned response in twoexperiments carried out four generations apart at generations 159 and 163 for (6 control +7 selected) replicate populations × 2 directions of conditioning × 3 replicate cages × 200flies (sexes mixed, aged 3-5 days). We analyzed the fixed effects of selection regime andgeneration, and random effect of replicate population nested within selection regime onthe conditioned response using a generalized linear mixed model (R 2.5.1, macro lmer)with binomial error distribution and logit link function (Pinheiro and Bates 2000). Theconditioned response (a measure of associative learning) is the proportion of eggs laid onthe substrate that was not associated with quinine during training.

Correlated responsesLongevity. To assay longevity, adult flies were aged in 1-l PVC cages with a 40-ml vialcontaining 10 ml food attached to the side. Food was changed three times per week. Wemeasured age-specific survival for (6 control + 7 selected) replicate populations × 2 sexes× 3 replicate cages × 100 virgin flies. Replicate cages were initiated at three consecutivedays from staggered cultures (1 cage per day). This assay was performed after 156generations of selection, followed by two generations without selection to reducematernal effects. We analyzed the fixed effects of selection regime and sex, and randomeffect of replicate population nested within selection regime on median longevity percage using a linear mixed model (R macro lme). For this analysis we ignored censoreddata. We did not use time to death (or censorship) as the response variable in a mixed-effects Cox model because the proportional hazards assumption was not met and becausethe R macro coxme does not yet support random slopes. To get additional insight in theage-specific effects of selection regime on death rates, we also analyzed the effects of ageand selection regime on mortality (WinModest, Pletcher 1999). For this analysis weincluded censored data but had to pool replicate populations. For each sex, we fitted alogistic model:

( )11 −+=bxbxxebsaaeμ, (1)

where μxis the instantaneous mortality rate at age x, a is the initial mortality rate, b is therate at which mortality increases with age, and s is the deceleration parameter. For eachsex and regime, this model was more parsimonious than a Gompertz model, where s = 0([ ]21χ≥ 187, P < 0.0001). Observed mortality rates were estimated by μx≈ −ln(px)/Δx,where pxis the proportion of flies surviving from age x to age x + Δx.Development and body mass. To assay development and body mass, eggs were laidwithin 12 hours by several hundred one-week-old flies and transferred in groups of 100 to68-ml replicate vials containing 10 ml food. We measured the time from egg to adulteclosion for (6 control + 7 selected) replicate populations × 8 replicate vials × 100 eggs.

6Adults were removed within 12 hours of eclosion, counted, sexed, dried at 80°C for threedays, and weighed (2 flies per replicate vial and sex) on a micro balance (MT5, Mettler-Toledo). This assay was performed after 123 generations of selection followed by twogenerations without selection. We analyzed the fixed effect of selection regime andrandom effect of replicate population nested within selection regime on the meandevelopment time per vial using a linear mixed model, and on the proportion of eggsdeveloped into adults per vial using a generalized linear mixed model with binomial errordistribution and logit link. We analyzed the fixed effects of selection regime and sex, andrandom effects of replicate vial nested within replicate population nested within selectionregime on dry body mass using a linear mixed model.Fecundity. To assay fecundity, flies were kept in mixed-sex groups of about 200individuals per 175-ml vial and transferred to new vials with fresh food every three days.One day before testing, flies were sexed using CO2anesthesia and females were placedsingly in 40-ml vials with food and fresh yeast to stimulate egg maturation. On a testingday, each female was allowed to oviposit in the dark for 20 hours in a 68-ml vialcontaining 10 ml grape juice jelled with agar (15 g/l), and fresh yeast to stimulateoviposition. We measured age-specific fecundity for 2 selection regimes × 6 replicatepopulations × 3 age classes (3, 10 and 24 days) × 20 mated females. Females of oneselected population were accidentally lost, so only six instead of seven selectedpopulations (plus six control populations) were included in this assay. This assay wasperformed after 154 generations of selection followed by three generations withoutselection. We analyzed the fixed effects of selection regime and age (as categoricalvariable), and random effect of replicate population nested within selection regime on thenumber of eggs laid, using a generalized linear mixed model with Poisson errordistribution and log link function.Desiccation resistance. To assay desiccation resistance, 3 to 6-day-old flies weresexed using CO2anesthesia one day before testing and females were placed in groups of50 per 40-ml vial with food. On a testing day, each group was transferred to an emptycage (l×w×h = 92×92×127 mm). We measured time to death for (5 control + 7 selected)replicate populations × 8 replicate cages (= 2 days × 4 cages) × 50 females. Females ofone control population were accidentally lost, so only five instead of six controlpopulations (plus seven selected populations) were included in this assay. This assay wasperformed after 110 generations of selection followed by two generations withoutselection. We analyzed the fixed effect of selection regime and random effect of replicatepopulation nested within selection regime on the mean time to death per cage using alinear mixed model.

SELECTION FOR INCREASED LIFE SPAN

Fly origin and maintenanceWe used two replicate population pairs of a long-lived population and an unselectedcontrol population, which were kindly provided by Robert Arking (Wayne StateUniversity, Detroit, MI). The origin and selection experiment has been described byArking and colleagues (Arking 1987; Luckinbill et al. 1984). In short, a base populationwas founded from about forty females collected in a Michigan peach orchard in the early1980’s. This base population was expanded and split into replicate populations. Aftereight generations, one selected and one control population were derived from each

7replicate population. Control and selected populations are therefore paired, in contrast topopulations selected for improved learning described above. Control populations (R)were maintained by rearing eggs laid by young adults, whereas selected populations (L)were created by rearing eggs laid by old adults. We received larvae from replicatepopulation pairs a and b after 258 (Ra), 125 (La), 257 (Rb) and 130 (Lb) generations ofselection. Larvae were transferred to a yeast-sucrose-cornmeal medium containing 2%w/v brewer’s yeast. This medium was also used to expand flies for two generations on a2-week cycle and to age the adults during the experiments. Adults were allowed to ecloseduring 24 h and were allowed to mate for two days, after which males were discarded.Adult females were aged as described above in groups of 200.

Direct responseLongevity was assayed in the same way as for flies selected for improved learning (seeabove), but was done on mated females only. We measured longevity for 2 replicatepopulation pairs × 2 selection regimes × 7 replicate cages × 200 once-mated females.Replicate cages were initiated within eleven days at four and three consecutive days (1cage per day). Due to low egg-to-adult viability of the Lb population, the firstdemography cage of this population was censored at 30 days for the odor avoidance assayat 32 days. We analyzed the fixed effect of selection regime and random effect ofreplicate population pair on the median longevity per cage using a linear mixed model.

Correlated responsesLearning ability. At three age classes (5, 19 and 32 days), we measured 1-h memoryusing Pavlovian conditioning with airborne odors as olfactory conditioned stimuli andmechanical shock as an aversive reinforcer (Mery and Kawecki 2005; Mery et al. 2007b).The first age class represents young but mature flies. At the age of 32 days flies can beconsidered middle-aged: more than 95% are still alive, but they already show declines invarious aspects of performance including learning (Grotewiel et al. 2005), and anincrease in mortality rates becomes apparent (Arking et al. 1996). We did not test olderflies or compare physiological ages because olfactory learning requires olfaction, shockresilience and locomotion, which become seriously impaired at more advanced ages. Twodays before testing, flies were anesthetized using CO2and transferred in groups of 50 to68-ml vials containing 10 ml food. At a testing day, flies were gently tapped withoutanesthesia into 10-ml test tubes and exposed to three consecutive training cycles. Eachtraining cycle consisted of 30 s of one odor accompanied by a mechanical shockdelivered by a test tube shaker (Heidolph Reax top; 2400 rpm in 5-mm orbit, or a relativecentrifugal force of about 5 × g) every 5 s for 1 s (CS+), followed by 60 s of humidifiedair (resting period), followed by 30 s of another odor without shock (CS−), againfollowed by 60 s of humidified air. Odors were delivered using gas-washing bottlescontaining either 4-methylcyclohexanol (MCH) or 3-octanol (OCT) (Sigma-Aldrich)dissolved in 500 ml mineral oil (Marcol 82, ExxonMobil) at a concentration of 0.6 ml/l.In half of the cases the shock was paired with MCH, in the other half with OCT. Flieswere tested in a T-maze 60 min after the end of conditioning by giving them a choicebetween the two odors for 60 s. After the test we counted the number of flies that chosethe odor previously associated with shock and the number that chose the other odor. Forthis experiment, we tested 2 replicate population pairs × 2 selection regimes × 3 age

8classes × 2 directions of conditioning × 12 replicates × 50 once-mated females.Replicates were tested on six consecutive days (2 replicates per day). We analyzed thefixed effects of selection regime and age, and random effect of replicate population pairon the conditioned response, using a generalized linear mixed model with binomial errordistribution and logit link function. The conditioned response is the proportion of flieschoosing the odor that was not associated with mechanical shock during training.Unconditioned response to odors. We measured the unconditioned response to odorsfor two reasons: first, to test for a potential correlated response in another component ofcognition to the effect of selection for extended longevity, and second, to check ifdifferences in learning were not confounded with differences in odor perception. Matedfemale flies were maintained and prepared as for the learning assay (see above), but theywere not trained, and were given a choice between one of the odorants (MHC or OCT)and the solvent (mineral oil) during testing in the T-maze. For this experiment, we tested2 odorants (MCH and OCT) × 2 replicate population pairs × 2 selection regimes × 3 ageclasses × (8 to 12) replicates × 50 once-mated females. The number of replicates varieddue to low egg-to-adult viability. For each odorant, we analyzed the fixed effectsselection regime and age, and random effect of replicate population pair on theunconditioned response, using a generalized linear model with binomial error distributionand logit link function. The unconditioned response is the proportion of unconditionedflies choosing the solvent over the odor.

STATISTICS

We already described for each experiment the response variable, predictor variables andthe model used to describe their relationship. In all cases, we then used Akaike’sInformation Criterion (AIC) to select the most parsimonious model. In the text we giveresults from partial deviance tests between the most parsimonious model and a similarmodel with the considered term omitted or added. For selection on learning, replicatepopulation was treated as a random effect nested within selection regime. For selectionon longevity, replicate population was also treated as a random effect but not nestedwithin selection regime because populations were paired. We also tested for interactionsbetween random and fixed effects, for example by comparing a random-intercept model( )XbYii 100ββ++=with a random-intercept and random-slope model( ) ( )XbbYiii 1100+++=ββ, where Y is the response variable, X is an explanatoryvariable, β are fixed-effect coefficients, and b0iand b1iare the effects of the ith randomlyselected factor level of a random-effect variable. This random-effect variable is normallydistributed with mean 0 and variances20bσand21bσ. For graphical presentation, weplotted the conditioned and unconditioned responses on a scale from −1 to 1 bymultiplying the proportion by 2 and subtracting 1. This is the standard scale in flylearning literature, on which a fifty-fifty distribution corresponds to a score of zero.

ResultsSELECTION FOR IMPROVED LEARNING

Direct responseFlies from high-learning populations indeed learned significantly better in the ovipositionlearning assay than flies from control populations (Fig. 1; GLMM on conditioned

Correlated responsesLearning ability. Flies from long-lived populations had a 39% reduction of one-hourmemory compared with flies from control populations (Fig. 5; GLMM on conditionedresponse, regime:[ ]21χ= 4.43, P = 0.035). This negative effect of selection regime onlearning ability was age dependent (regime × age interaction:[ ]21χ= 8.99, P = 0.0027).Analysis by age class revealed that the negative effect of selection regime disappearedwith age (age 5 days:[ ]21χ= 4.27, P = 0.039; age 19 days:[ ]21χ= 0.78, P = 0.38; age 32days:[ ]21χ= 0.21, P = 0.65). Overall, learning ability declined with age ([ ]21χ= 20.6, P <0.0001). The effect of replicate population pair on the variation in overall learningperformance was marginally significant ([ ]21χ= 3.23, P = 0.072) but replicate populationpair contributed significantly to the variation in the effect of selection regime on learningperformance ([ ]21χ= 1163, P < 0.0001). Treating age as a categorical instead of acontinuous predictor gave similar results but did not enhance the parsimony of the model([ ]22χ= 1.20, P = 0.55), suggesting that the effect of age was approximately linear (on a

11logit scale). The difference in learning ability between selection regimes cannot beconfounded with a difference in survival, because all learning tests were performedbefore the difference in survival became noticeable (Fig. 4).Unconditioned response. In contrast to the unambiguous effect of selection regime onlearning ability in both replicate population pairs, there was no clear effect of selectionregime on the unconditioned response (Fig. 6). There was a significant interactionbetween selection regime and age for the unconditioned response to MCH (Fig. 6A;GLMM,[ ]21χ= 13.9, P = 0.0010) and no effect of selection regime on the unconditionedresponse to OCT (Fig. 6B;[ ]21χ= 0.003, P = 0.96). Additionally, treating age as acategorical instead of a continuous predictor enhanced the parsimony of both models(MCH:[ ]23χ= 46.8, P < 0.0001; OCT:[ ]22χ= 4490, P < 0.0001), suggesting that theeffect of age was nonlinear. Moreover, there were significant interactions between therandom effect of replicate population pair and the fixed effects of selection regime(MCH:[ ]21χ= 21.1, P < 0.0001; OCT:[ ]21χ= 98.0, P < 0.0001), age class (MCH:[ ]22χ=9.60, P = 0.0082) and regime-by-age interaction (OCT:[ ]22χ= 26.6, P < 0.0001). On theone hand, this implies that the effect of selection for increased life span on chemotaxis iscomplex. On the other hand, these data suggest that the negative effect of selection onlearning ability (Fig. 5) cannot be explained by poorer responsiveness to odors (Fig. 6). Ifanything, long-lived populations tended to show a stronger response to odors. In addition,there was no correlation between the effect of selection regime on learning ability and itseffect on odor avoidance, and doubling the MCH concentration in aged flies did notcritically affect learning scores (data not shown).

DiscussionIn this study we report that fly populations selected for improved learning lived shorterthan their unselected controls, and fly populations selected for extended longevity hadreduced learning ability early in life relative to their controls. Our results indicate asymmetrical evolutionary trade-off between learning ability and life span. Othercorrelated responses to selection for improved learning were a minor reduction infecundity at late age and possibly a small increase in dry adult mass.These results are consistent with Williams’ ninth prediction that “successful selectionfor increased longevity should result in decreased vigor in youth” (Williams 1957) andsuggest that the response to selection in both experiments was based on genes withantagonistic pleiotropic effects on both learning ability and life span. Genes with suchantagonistic pleiotropic effects on performance at young versus old age are thought to beresponsible for the evolution of aging (Partridge and Barton 1993; Williams 1957). Suchpleiotropy may reflect reallocation of resources from somatic maintenance and repair toacquisition, retaining and retrieval of information (or vice versa). It may also be due todesign trade-offs, e.g., one might speculate that increased neuronal activity generatesgreater oxidative damage, accelerating neuron death. Finally, longevity might be affectedindirectly through potential changes in behavior such as feeding, since restricting foodintake extends longevity in diverse taxa including flies (Partridge et al. 2005).Although no specific alleles with antagonistic effects on longevity and learning abilityhave been identified, various pleiotropic effects are often observed for alleles that affect

12learning ability (Dubnau and Tully 1998; Mery et al. 2007a) and life span (Nuzhdin et al.1997). Indirect evidence suggests that antagonistic pleiotropy is ubiquitous (Campisi2003; Leroi et al. 2005). One candidate pleiotropic gene with antagonistic effects onlearning and longevity in Drosophila is S6 kinase: S6kII is necessary for operant learning(Putz et al. 2004), whereas dominant-negative overexpression of dS6k extends longevity(Kapahi et al. 2004). Other Drosophila candidate genes are ab and Gef64C because theywere associated with increased life span in a P-element screen (Magwire and Mackay2006) and they have pleiotropic effects on the nervous system.An alternative explanation for the apparent trade-off between learning and longevitywould be linkage disequilibrium between genes that affect learning and genes that affectlongevity, either by chance or due to selection. This is less likely because populationswere kept at fairly large population sizes for several generations before the start ofselection, allowing ample opportunity for recombination. There is also no reason tobelieve why selection would have favored negative linkage disequilibrium between genesaffecting learning ability and longevity in the base populations of both sets of selectionlines.Differences between selection regimes can potentially be confounded by effects ofinbreeding. As a by-product of selection, selected populations may have had smallereffective sizes than control populations. Although we did not test for this alternative inthis study, previous studies indicate that a substantial effect of inbreeding is unlikely.Specifically, in a previous assay on F1hybrids between our replicate high-learningpopulations, no inbreeding depression was detected for larval competitive ability,fecundity or learning ability (Kawecki and Mery 2006; Mery and Kawecki 2003). The F1

hybrids between some pairs of high-learning populations actually showed outbreedingdepression for learning ability (Kawecki and Mery 2006). Performance of F1hybridsbetween the replicate long-lived populations that we used has not been reported.However, in a similar selection experiment on late reproduction, F1hybrids betweenreplicate populations did not differ from parental populations in ovary weight orstarvation resistance (Hutchinson and Rose 1991). In another selection experiment on latereproduction there was evidence for differential inbreeding, but the direction was sexspecific and the F1hybrids between selected populations still lived significantly longerthan the F1hybrids between control populations (Roper et al. 1993). Furthermore, if theresponses to selection were caused by differential inbreeding, one would expectcorrelated traits to respond in the same direction as the trait under direct selection.Instead, we found a trade-off in both selection experiments.Although the long-lived populations learned considerably less well at young age, theyshowed a slower decline of their learning ability with age, so that at 5 weeks theirlearning performance was as good as that of the control populations (Fig. 5). This isconsistent with some studies of long-lived mutants in nematodes (Murakami et al. 2005),flies (Juliette Pont, unpublished data), and mice (Bartke 2005), which also show a slowerage-related decline in learning. This would suggest that the mechanisms underlyingdemographic and cognitive aging overlap. However, another Drosophila mutant has beenfound to show a slower age-related decline in learning without life-span extension(Yamazaki et al. 2007).The reduction in longevity in flies from high-learning populations was significantlylarger in females than in males. This may be due to the fact that selection for improved

13learning was based on oviposition-substrate choice and was therefore imposed on femalesonly. As a result, selection may have acted on genes with female-biased expression. Suchgenes are ubiquitous in the Drosophila genome (Arbeitman et al. 2002).We observed a reduction in longevity without substantial responses in fecundity anddevelopment. Although longevity is often genetically correlated with fecundity (e.g.,Rose 1984), development rate (e.g., Partridge and Fowler 1992) and stress resistance(e.g., Service et al. 1985), our results support previous studies (Bubliy and Loeschcke2005) that these traits can also evolve independently. Moreover, whereas an increase inlongevity is usually associated with a decrease in fecundity, a decrease in longevity is notnecessarily associated with an increase in fecundity (Zwaan et al. 1995). The trendtowards an increased body mass in response to selection for improved learning, ifreflecting a real difference, might be an allometric growth effect of an enlarged nervoussystem. An allometric enlargement of the hippocampus has been reported in food-cachingbird species compared with non-caching relatives (Krebs et al. 1989).We observed that the oldest flies showed the highest odor avoidance, whereasolfaction usually senesces (Cook-Wiens and Grotewiel 2002). Odor sensitivity may haveincreased because flies became sperm depleted (Anton et al. 2007). Nevertheless, themain objective of the olfaction assay was to exclude reduced olfaction as a confoundingexplanation of reduced learning performance.We conclude that there is a symmetrical evolutionary trade-off between learningability and life span in Drosophila. This study adds to our understanding of theevolutionary costs of learning (Dukas 2004) and the evolutionary links betweendemographic and cognitive traits (Horiuchi and Saitoe 2005).

ACKNOWLEDGMENTS

We would like to thank R. Arking for the flies selected for increased life span and helpfuldiscussion, K. Hughes and two anonymous reviewers for insightful comments on themanuscript, S. Rion, G. Schwaller and A. Dybek for helping to collect data, L. Sygnarskifor maintaining the flies selected for improved learning, K.J. Min for a food recipe and A.Werro for making the demography cages. This research was supported by grants from theSwiss National Science Foundation and the Velux Foundation to TJK.